Computer-Science Reinforcement Learning Got Rewards Wrong (gist.github.com)

Ben Recht's recent blog post challenges the foundational understanding of rewards in Reinforcement Learning (RL), arguing that the prevailing belief of rewards functioning as external validations is fundamentally flawed. Traditionally, RL has modeled rewards as intrinsic to the environment, with agents responding to these signals to maximize long-term gains. However, Recht posits that rewards should instead be conceptualized as internal mechanisms of the agent itself, suggesting a shift whereby agents interpret changes in their environment to determine their own reward signals. This alteration not only aligns more closely with cognitive theories but empowers agents to formulate diverse strategies based on their individual goals, ultimately leading to richer learning experiences. The significance of this new perspective lies in its potential to enhance RL methodologies and outcomes. By recognizing rewards as a product of the agent's internal processing, it paves the way for agents to operate under varied goals while observing the same environment, thus encouraging personalized learning paths. Furthermore, this approach opens up possibilities for dynamic and learnable reward systems, allowing for more sophisticated decision-making mechanisms within the agents. Ultimately, Recht's proposition invites the AI/ML community to rethink the role of rewards, which could lead to more robust and effective RL applications across various domains.